{"id":13689209,"url":"https://github.com/aramis-lab/clinica","last_synced_at":"2025-08-07T05:30:34.291Z","repository":{"id":36030892,"uuid":"187224566","full_name":"aramis-lab/clinica","owner":"aramis-lab","description":"Software platform for clinical neuroimaging 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markdownlint-disable MD033 --\u003e\n\n\u003ch1 align=\"center\"\u003e\n  \u003ca href=\"https://www.clinica.run\"\u003e\n    \u003cimg src=\"https://www.clinica.run/assets/images/clinica-icon-257x257.png\" alt=\"Logo\" width=\"120\" height=\"120\"\u003e\n  \u003c/a\u003e\n  \u003cbr/\u003e\n  Clinica\n\u003c/h1\u003e\n\n\u003cp align=\"center\"\u003e\u003cstrong\u003eSoftware platform for clinical neuroimaging studies\u003c/strong\u003e\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/aramis-lab/clinica/actions/workflows/test.yml\"\u003e\n    \u003cimg src=\"https://github.com/aramis-lab/clinica/actions/workflows/test.yml/badge.svg\" alt=\"Build Status\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://badge.fury.io/py/clinica\"\u003e\n    \u003cimg src=\"https://badge.fury.io/py/clinica.svg\" alt=\"PyPI version\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://pypi.org/project/clinica\"\u003e\n    \u003cimg src=\"https://img.shields.io/pypi/pyversions/clinica\" alt=\"Supported Python versions\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://aramislab.paris.inria.fr/clinica/docs/public/latest/Installation/\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://aramislab.paris.inria.fr/clinica/docs/public/latest/Installation/\"\u003e\n    \u003cimg src=\"https://anaconda.org/aramislab/clinica/badges/platforms.svg\" alt=\"platform\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://github.com/psf/black\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/code%20style-black-000000.svg\" alt=\"Code style: black\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://pepy.tech/project/clinica\"\u003e\n    \u003cimg src=\"https://static.pepy.tech/badge/clinica/month\" alt=\"Downloads\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://www.clinica.run\"\u003eHomepage\u003c/a\u003e |\n  \u003ca href=\"https://aramislab.paris.inria.fr/clinica/docs/public/latest/\"\u003eDocumentation\u003c/a\u003e |\n  \u003ca href=\"https://doi.org/10.3389/fninf.2021.689675\"\u003ePaper\u003c/a\u003e |\n  \u003ca href=\"https://github.com/aramis-lab/clinica/discussions\"\u003eForum\u003c/a\u003e |\n  See also:\n  \u003ca href=\"#related-repositories\"\u003eAD-ML\u003c/a\u003e,\n  \u003ca href=\"#related-repositories\"\u003eAD-DL\u003c/a\u003e,\n  \u003ca href=\"#related-repositories\"\u003eClinicaDL\u003c/a\u003e\n\u003c/p\u003e\n\n## About The Project\n\nClinica is a software platform for clinical research studies involving patients\nwith neurological and psychiatric diseases and the acquisition of multimodal\ndata (neuroimaging, clinical and cognitive evaluations, genetics...),\nmost often with longitudinal follow-up.\n\nClinica is command-line driven and written in Python.\nIt uses the [Nipype](https://nipype.readthedocs.io/) system for pipelining and combines\nwidely-used software packages for neuroimaging data analysis\n([ANTs](https://stnava.github.io/ANTs/),\n[FreeSurfer](https://surfer.nmr.mgh.harvard.edu/),\n[FSL](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki),\n[MRtrix](https://www.mrtrix.org/),\n[PETPVC](https://github.com/UCL/PETPVC),\n[SPM](https://www.fil.ion.ucl.ac.uk/spm/)), machine learning\n([Scikit-learn](https://scikit-learn.org/stable/)) and the [BIDS\nstandard](https://bids-specification.readthedocs.io/) for data organization.\n\nClinica provides tools to convert publicly available neuroimaging datasets into\nBIDS, namely:\n\n- [ADNI: Alzheimer’s Disease Neuroimaging Initiative](https://aramislab.paris.inria.fr/clinica/docs/public/latest/Converters/ADNI2BIDS/)\n- [AIBL: Australian Imaging, Biomarker \u0026 Lifestyle Flagship Study of Ageing](https://aramislab.paris.inria.fr/clinica/docs/public/latest/Converters/AIBL2BIDS/)\n- [HABS: Harvard Aging Brain Study](https://aramislab.paris.inria.fr/clinica/docs/public/latest/Converters/HABS2BIDS/)\n- [NIFD: Neuroimaging in Frontotemporal Dementia](https://aramislab.paris.inria.fr/clinica/docs/public/latest/Converters/NIFD2BIDS/)\n- [OASIS: Open Access Series of Imaging Studies](https://aramislab.paris.inria.fr/clinica/docs/public/latest/Converters/OASIS2BIDS/)\n- [OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer’s Disease](https://aramislab.paris.inria.fr/clinica/docs/public/latest/Converters/OASIS3TOBIDS/)\n\nClinica can process any BIDS-compliant dataset with a set of complex processing\npipelines involving different software packages for the analysis of\nneuroimaging data (T1-weighted MRI, diffusion MRI and PET data).\nIt also provides integration between feature extraction and statistics, machine\nlearning or deep learning.\n\n![ClinicaPipelines](https://www.clinica.run/img/Clinica_Pipelines_A4_2021-04-02_75dpi.jpg)\n\nClinica is also showcased as a framework for the reproducible classification of\nAlzheimer's disease using\n[machine learning](https://github.com/aramis-lab/AD-ML) and\n[deep learning](https://github.com/aramis-lab/clinicadl).\n\n## Getting Started\n\n\u003e Full instructions for installation and additional information can be found in\nthe [user documentation](https://aramislab.paris.inria.fr/clinica/docs/public/latest/).\n\n### Using pipx (recommended)\n\nClinica can be easily installed and updated using [pipx](https://pypa.github.io/pipx/).\n\n```console\npipx install clinica\n```\n\n### Using pip\n\n```console\npip install clinica\n```\n\n### Using Conda\n\nClinica relies on multiple third-party tools to perform processing.\n\nAn environment file is provided in this repository\nto facilitate their installation in a [Conda](https://docs.conda.io/en/latest/miniconda.html) environment:\n\n```console\ngit clone https://github.com/aramis-lab/clinica \u0026\u0026 cd clinica\nconda env create\nconda activate clinica\n```\n\nAfter activation, use `pip` to install Clinica.\n\n### Additional dependencies (required)\n\nDepending on the pipeline that you want to use, you need to install pipeline-specific interfaces.\nSome of which uses a different runtime or use incompatible licensing terms, which prevent their distribution alongside Clinica.\nNot all the dependencies are necessary to run Clinica.\nPlease refer to this [page](https://aramislab.paris.inria.fr/clinica/docs/public/dev/Software/Third-party/)\nto determine which third-party libraries you need to install.\n\n## Example\n\nDiagram illustrating the Clinica pipelines involved when performing a group\ncomparison of FDG PET data projected on the cortical surface between patients\nwith Alzheimer's disease and healthy controls from the ADNI database:\n\n![ClinicaExample](https://www.clinica.run/img/Clinica_Example_2021-04-02_75dpi.jpg)\n\n1. Clinical and neuroimaging data are downloaded from the ADNI website and data\n   are converted into BIDS with the [`adni-to-bids`\n   converter](https://aramislab.paris.inria.fr/clinica/docs/public/latest/Converters/ADNI2BIDS/).\n2. Estimation of the cortical and white surface is then produced by the\n   [`t1-freesurfer`\n   pipeline](https://aramislab.paris.inria.fr/clinica/docs/public/latest/Pipelines/T1_FreeSurfer/).\n3. FDG PET data can be projected on the subject’s cortical surface and\n   normalized to the FsAverage template from FreeSurfer using the\n   [`pet-surface` pipeline](https://aramislab.paris.inria.fr/clinica/docs/public/latest/Pipelines/PET_Surface/).\n4. TSV file with demographic information of the population studied is given to\n   the [`statistics-surface`\n   pipeline](https://aramislab.paris.inria.fr/clinica/docs/public/latest/Pipelines/Stats_Surface/) to generate\n   the results of the group comparison.\n\n\u003e For more examples and details, please refer to the\n\u003e [Documentation](https://aramislab.paris.inria.fr/clinica/docs/public/latest/).\n\n## Support\n\n- Check for [past answers](https://groups.google.com/forum/#!forum/clinica-user) in the old Clinica Google Group\n- Start a [discussion](https://github.com/aramis-lab/clinica/discussions) on Github\n- Report an [issue](https://github.com/aramis-lab/clinica/issues) on GitHub\n\n## Contributing\n\nWe encourage you to contribute to Clinica!\nPlease check out the [Contributing to Clinica guide](CONTRIBUTING.md) for\nguidelines about how to proceed.  Do not hesitate to ask questions if something\nis not clear for you, report an issue, etc.\n\n## License\n\nThis software is distributed under the MIT License.\nSee [license file](https://github.com/aramis-lab/clinica/blob/dev/LICENSE.txt)\nfor more information.\n\n## Citing us\n\n- Routier, A., Burgos, N., Díaz, M., Bacci, M., Bottani, S., El-Rifai O., Fontanella, S., Gori, P., Guillon, J., Guyot, A., Hassanaly, R., Jacquemont, T.,  Lu, P., Marcoux, A.,  Moreau, T., Samper-González, J., Teichmann, M., Thibeau-Sutre, E., Vaillant G., Wen, J., Wild, A., Habert, M.-O., Durrleman, S., and Colliot, O.:\n*Clinica: An Open Source Software Platform for Reproducible Clinical Neuroscience Studies* Frontiers in Neuroinformatics, 2021\n[doi:10.3389/fninf.2021.689675](https://doi.org/10.3389/fninf.2021.689675)\n\n## Related Repositories\n\n- [AD-DL: Classification of Alzheimer's disease status with convolutional neural networks](https://github.com/aramis-lab/AD-DL).\n- [AD-ML: Framework for the reproducible classification of Alzheimer's disease using\nmachine learning](https://github.com/aramis-lab/AD-ML).\n- [ClinicaDL: Framework for the reproducible processing of neuroimaging data with deep learning methods](https://github.com/aramis-lab/clinicadl).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faramis-lab%2Fclinica","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faramis-lab%2Fclinica","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faramis-lab%2Fclinica/lists"}